Single-cell and spatial transcriptomics

scGFT generates synthetic single-cell RNA-seq data without training, preserving natural gene expression profiles to address data scarcity in AI-driven biomedical research.

moscot unifies optimal transport for temporal, spatial and spatiotemporal single-cell mapping, using multimodal cost spaces and low-rank solvers to scale to 1.7 million mouse embryo cells.

This paper introduces Chronocell, a method for trajectory inference using process time modeling, showing it can interpolate between trajectory inference and clustering and assess dataset suitability.

This study introduces scSGS, a method using single-cell RNA sequencing to identify gene function by analyzing stochastic gene silencing and differentially expressed genes.

The paper performs snRNA-seq on human lateral septum tissue to identify cell types and marker genes, finding conserved and species-specific expression patterns.

BuDDI predicts cell-type-specific perturbations from bulk data by integrating single-cell and bulk profiles using domain adaptation in a variational autoencoder.

This study maps the temporal sequence of mouse lung regeneration after viral injury, identifying persistent injury-associated cell states and their role in alveolar niche repair.

p-ClustVal improves clustering of scRNASeq data using a p-adic transformation, enhancing performance in 91% of cases across 30 experiments.

TraCSED identifies resistance pathways in breast cancer by analyzing scRNA-seq data and clonal selection dynamics over time.

This study integrates spatial transcriptomics and single-cell data from gastric cancer samples to reveal tumor ecosystem dynamics, evolutionary trajectories, and immune microenvironment features.

This study maps the molecular and cellular dynamics of the developing human neocortex using single-nucleus data, revealing gene regulatory networks and a cancer-related progenitor subtype.

UCS improves cell segmentation for subcellular spatial transcriptomics by integrating nuclei and transcript data, enhancing accuracy and enabling downstream analyses.

This study adapts the ACME HS method to isolate intact single cells from fresh-frozen endocrine tissues, showing it preserves cell integrity and gene expression better than other methods.

This study introduces a Bayesian Dirichlet-multinomial regression model to integrate single-cell RNA sequencing data with clinical variables, uncovering cell-type-specific associations across three diseases.

PACS is a statistical model for analyzing snATAC-seq data that improves power and controls false positives in differential accessibility analysis.

STARLING quantifies cell populations from spatial protein expression data while accounting for segmentation errors, identifying known cell types and quantifying heterogeneity in human tonsil tissue.

CryoSCAPE enables scalable immune profiling using cryopreserved whole blood for multi-omic single cell and functional assays, demonstrating compatibility with high-throughput RNA sequencing and functional testing.

LEMUR identifies gene expression changes across conditions using a continuous latent space, improving analysis of multi-condition single-cell RNA-seq data.

The paper introduces CRAWDAD, a tool for analyzing cell-type spatial relationships across scales in spatially resolved omics data, and demonstrates its application on simulated and real datasets.

The paper develops a method to annotate cell types across species using orthologous marker gene groups, identifying conserved cell-type markers in 15 plant species.

Stator identifies cell types, subtypes, and states in single-cell data by analyzing higher-order gene expression dependencies, revealing cryptic states and subtypes not captured by proximity-based clustering.

The paper uses spatial transcriptomics to map cell types and their spatial organization in healthy and fibrotic human liver, identifying changes in hepatocyte populations during fibrosis.

This paper introduces a VAE-based model for integrating multimodal single-cell data, showing it can map queries across modalities and improve data imputation and biological discovery.

This paper introduces GBCD to decompose tumor transcriptional heterogeneity in scRNA-seq data, identifying a new prognostic gene expression program in pancreatic cancer.

The paper uses single-cell transcriptomics to analyze stress responses in HepaRG cells exposed to various chemicals, identifying distinct phenotypic groups and dynamic shifts in cellular subpopulations.

The paper introduces a machine learning framework to identify multiscale cellular interactions in spatial-omics data, revealing both known and novel communication patterns across tissues.

The paper introduces SciDAP, a platform enabling biologists to analyze single-cell sequencing data without coding expertise using reproducible pipelines.

This study uses single-cell transcriptomics to analyze Ochromonas triangulata and its associated bacterial community, identifying novel transcriptional states and metabolic shifts across growth phases.

SPEX provides a web-based platform for spatial omics analysis, enabling modular and user-friendly processing of spatial transcriptomics and proteomics data.

SeuratExtend integrates R and Python tools with databases to streamline scRNA-seq analysis, offering enhanced visualization and functional analysis capabilities.